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Abstract

Background—Serum biomarkers may identify people at risk for cardiovascular (CV) outcomes. Biobanked serum samples from 8494 participants with dysglycemia in the completed Outcome Reduction With Initial Glargine Intervention trial were assayed for 284 biomarkers to identify those that could identify people at risk for a CV outcome or death when added to clinical measurements.

Methods and Results—A multiplex analysis measured a panel of cardiometabolic biomarkers in 1 mL of stored frozen serum from every participant who provided biobanked blood. After eliminating undetectable or unanalyzable biomarkers, 8401 participants who each had a set of 237 biomarkers were analyzed. Forward-selection Cox regression models were used to identify biomarkers that were each independent determinants of 3 different incident outcomes: (1) the composite of myocardial infarction, stroke, or CV death; (2) these plus heart failure hospitalization or revascularization; and (3) all-cause death. When added to clinical variables, 10 biomarkers were independent determinants of the 1405 CV composite outcomes observed during follow-up; 9 biomarkers (including 8 of these 10) were independent determinants of the 2435 expanded composite outcomes; and 15 (including the 10 CV composite biomarkers) were independent determinants of the 1340 deaths. Adjusted C statistics increased from 0.64 for the clinical variables to 0.71 and 0.68 for the 2 CV composite outcomes, respectively, with the greatest increase to 0.75 for death (P<0.001 for the change).

Conclusions—A systematic hypothesis-free approach identified combinations of up to 15 cardiometabolic biomarkers as independent determinants of CV outcomes or death in people with dysglycemia.

Introduction

Impaired fasting glucose, impaired glucose tolerance, and diabetes mellitus independently increase the risk of cardiovascular (CV) outcomes, and diabetes mellitus reduces life expectancy by ≤15 years.1–4 Moreover, despite recent reductions in the incidence of CV outcomes in people with diabetes mellitus, both diabetes mellitus and lesser degrees of dysglycemia remain independent CV risk factors.5–7 The pathophysiologic reasons for this excess and for variations in incidence among people who already have established dysglycemia remain unclear. A “hypothesis-free” or “agnostic” approach could identify new biochemical markers of CV risk, which may in turn be indicators of hitherto unknown pathogenic mechanisms. It could also suggest new therapies or ways of monitoring the effect of established therapies on CV prevention.

Recent advances in the simultaneous measurement of large numbers of serum biomarkers covering various pathophysiological pathways in small volumes of serum have facilitated the ability to identify new markers for CV outcomes or death.8 This new technology can be used to analyze biobanked samples of serum from prospectively followed participants who had CV outcomes or who died during follow-up. A large panel of 284 cardiometabolic biomarkers was therefore assayed in biobanked serum from 8494 carefully characterized and followed participants in the recent Outcome Reduction With Initial Glargine Intervention (ORIGIN) trial. Results of these assays were then statistically analyzed to identify those biomarkers that independently provided better estimates of the risk of future CV outcomes or death than could be estimated from routinely measured clinical and biochemical data alone.

Methods

The ORIGIN trial design and final results have been published.9,10 Briefly 12 537 people with diabetes mellitus, impaired glucose tolerance, or impaired fasting glucose and additional CV risk factors were recruited and followed for a median of 6.2 years. Adjudicated outcomes included the composite of nonfatal myocardial infarction, nonfatal stroke, or CV death; an expanded composite outcome that also included heart failure hospitalization or revascularization; and death. The trial was approved by each site’s institutional review boards, and all of the participants provided written, informed consent. At the time of consent, a subset of 8494 (68%) also provided written consent for the collection and storage of a blood sample for future measurement of CV risk factors. Baseline fasting samples were collected, spun, separated, and divided into 1.5- to 2.0-mL aliquots; frozen within 2 hours of collection at between –20°C and –70°C; transported on either dry ice or in nitrogen vapor shippers to the Population Health Research Institute biobank in Hamilton; and then stored in nitrogen vapor-cooled tanks at –160°C. After completion of the ORIGIN trial, a single coded aliquot of 1 mL of serum from each participant (ie, with no associated clinical, laboratory, or other phenotypic information) was transported to Myriad RBM Inc (Austin, TX) for the multiplex analysis of a panel of 284 biomarkers, which were measured and then reported back to the Population Health Research Institute for subsequent statistical analyses.

Biomarker Platform and the Panel of Biomarkers

Using a customized Human Discovery Multi-Analyte Profile (MAP) 250+ panel on the LUMINEX 100/200 platforms, Myriad RBM Inc measured a panel of 284 biomarkers in a serum sample from each participant; the methodology used and the list of measured biomarkers appears in the Supplemental Methods section, available in the online-only Data Supplement. The panel included a combination of assays that had already been developed plus additional biomarker assays that were newly developed for this study by Myriad RBM Inc. Many of the components of the biomarker panel were selected based on their role in physiological systems relevant to CV disease (eg, inflammation, coagulation, endothelial function, renal function, oxidative stress, adipocyte biology, angiogenesis, β-cell biology, tissue repair, and iron metabolism).

Statistical Analysis

Determining the Distribution of Each Biomarker

Before any statistical analyses, the results for each of the 284 biomarkers were scrutinized and standardized independently by 4 authors masked to phenotype (M.J.M., G.P., S.H., and H.C.G.), who then finalized the list of biomarkers and participants who were included in the analyses as described in the online-only Data Supplement. Thus, data from 8401 participants with 237 reported biomarker levels were analyzed.

Identifying Significant Biomarkers

Participants were randomly divided (stratified by geographical region) into model building and model assessment groups composed of 5630 people (ie, 67%) and 2771 people (33%), respectively. Using the model building subset, those biomarkers that independently predicted each of the 3 outcomes (ie, the 2 CV composite outcomes and death) after accounting for clinical risk factors11 were identified using a Cox regression model for each outcome. The clinical risk factors included in each Cox model (ie, the basic clinical model) were sex plus another 7 CV risk factors identified in the validated INTERHEART risk score12 and available in the database. These included older age (men >55 years or women >65 years), previous CV event, previous diabetes mellitus, previous hypertension, current smoking, clinical history or laboratory evidence of microalbuminuria or macroalbuminuria, and low-density lipoprotein/high-density lipoprotein cholesterol ratio. A forward-selection approach was then used to identify those biomarkers that independently and cumulatively predicted the dependent variable, with a P value for inclusion set below 0.05 divided by 237 (ie, 0.00021) to account for the 237 comparisons. The possibility that inclusion of both continuous and ordinal biomarkers in the same regression would reduce the likelihood of identifying the significant ordinal biomarkers was explored by converting all of the biomarkers to 5-level ordinal variables and rerunning the model as a sensitivity analysis. The biomarkers identified in the model building group for each outcome were further assessed by including them in Cox models that were run on the model assessment group participants.

Tests for the global proportionality of each Cox model and supremum tests for proportionality of each independent variable were done and used a nominal P value of 0.05; Schoenfeld residuals for each independent variable were also plotted and scrutinized. If nonproportionality was identified, the interaction of time with each of the nonproportional independent variables was added to the model. C statistics with 95% confidence intervals (CIs) were calculated for Cox models with the clinical risk factors alone and with the clinical risk factors plus the identified biomarkers, and the improvement in the ability of the model to predict outcomes was summarized using the net reclassification improvement (NRI) based on classifying people into 4 categories of risk. These categories were defined by model-estimated probabilities of developing the outcomes of 0.05, 0.10, and 0.20. This number reflects the degree to which the full model versus the basic clinical model improves the ability to correctly predict people who will and will not have an outcome.13–15 Reclassification was also assessed by calculating the integrated discrimination improvement, which estimates the difference in discrimination slopes between the basic clinical model and the full model.16

Developing and Validating the Final Model

The forward-selection process described above was then run using the entire sample of 8401 people. Optimism-adjusted C statistics, NRIs, and the integrated discrimination improvement statistics for this final model were estimated by the following methods: (1) repeating the forward selection process in 1000 samples that were derived using bootstrapping with replacement, (2) estimating the difference between the statistic in each of those models and that computed from the model with 8401 unique individuals, (3) averaging the difference in these 1000 statistics, and (4) subtracting the average from the original statistic computed using 8401 unique individuals.17

Sensitivity analyses exploring the effect of the variables included or omitted from the clinical risk factors were conducted by rerunning the forward selection after substituting age for age category and including both age and serum creatinine in a 9-point clinical risk factor model. Calibration of the models was assessed by computing the slopes and 95% CIs of the predicted versus observed incidence of each outcome.14,18 All of the statistical analyses were done using SAS version 9.2 for UNIX (SAS Institute Inc, Cary, NC).

Results

The 8401 participants included in the ORIGIN biomarker study (66% men; mean age, 63.2 years; 59% with a previous CV event) were similar to all of the ORIGIN participants with respect to baseline characteristics. They experienced 1405 composite CV outcomes, 2435 expanded composite CV outcomes, and 1340 deaths during the ORIGIN trial. Key clinical characteristics of these individuals and the randomly chosen model building and assessment subsets are shown in Table 1.

Table IA in the online-only Data Supplement, displays the findings from Cox proportional hazards models built using the model building set. After forcing in the clinical risk factors, 8 of a possible 237 biomarkers each independently improved the ability to predict the CV composite outcome (P<0.00021). Three of these 8 biomarkers plus an additional 4 (ie, a total of 7) were identified when the process was repeated for the expanded CV composite outcome. When the process was repeated for death as the dependent variable, there was evidence of nonproportionality of the Cox model (P=0.02). After including the nonproportional variables plus 3 additional variables representing their interactions with time, a total of 10 biomarkers linked to death were identified. The discriminative ability of the models that included the clinical variables plus identified biomarkers was clearly greater than that of the models with the clinical variables alone (NRI statistics for the composite, expanded composite, and death outcomes were 0.28, 0.12, and 0.37, respectively). The models generated similar hazard ratios (Table IB in the online-only Data Supplement) and performed similarly when they were assessed in the 2770 participants from the assessment set, whose data were not used to develop these models (Table 2).

The forward-selection process was then applied to the entire data set of 8401 people for each of these 3 outcomes (Table 3 and Table II in the online-only Data Supplement). This identified 10 biomarkers for the CV composite outcome including N-terminal pro-B-type natriuretic peptide (NT-proBNP), trefoil factor 3, apolipoprotein B, angiopoietin-2, osteoprotegerin, α-2-macroglobulin, glutathione S transferase α, growth/differentiation factor 15, hepatocyte growth factor receptor, and chromogranin A (the first 7 of which were identified in the model building group). This process also identified 9 biomarkers for the expanded CV composite (4 of which were identified by the first approach based on the model building set) and 15 biomarkers for mortality (9 of which were identified in the model building group). Similar but not identical sets of biomarkers were identified in sensitivity analyses conducted using a modified basic clinical model. For example, the differences noted after adding serum creatinine and substituting age for age category are shown in Table III in the online-only Data Supplement.

Three biomarkers (NT-proBNP, angiopoietin 2, and glutathione S transferase α) were persistently identified in all of the models and for all of the outcomes. The possibility that NT-proBNP (one of the strongest predictors of all 3 outcomes that has been linked previously to CV outcomes) could account for most of the discriminative ability of the identified biomarkers was also assessed. However, the discriminative ability of the models that included the clinical variables plus all of the identified biomarkers (Table IV in the online-only Data Supplement) was clearly greater than that of the models with the clinical variables plus just NT-proBNP (NRI statistics for the composite, expanded composite, and death outcomes were 0.14, 0.08, and 0.28, respectively).

Although somewhat different biomarkers were included in the models built on all 8401 participants compared with the models built on 5630 participants in the model building set, their discriminative abilities were similar (Table 2). For example, the C statistic for the composite CV outcome in the model built on all of the participants increased from 0.64 (95% CI, 0.63–0.65) with the clinical variables alone to an optimism-adjusted value of 0.71 (95% CI, 0.69–0.72) when the 10 identified biomarkers were added to these clinical variables, and the optimism-adjusted NRI was 0.22 (95% CI, 0.19–0.25).

Figure 1 illustrates Kaplan–Meier curves for the composite CV outcome in 5 groups of people defined according to fifths of the predicted risk probability estimated using the baseline clinical model alone and on fifths using the clinical variables plus the identified biomarkers. Similar figures for the other outcomes are shown in Figure I in the online-only Data Supplement. When each of the 3 components of the CV composite outcome was examined separately in exploratory analyses (Table 2), the model predicted CV death (optimism-adjusted NRI, 0.52 [95% CI, 0.47–0.57]) better than the other 2 components.

Proportions free of the cardiovascular (CV) composite outcome at each time point are displayed. A, Kaplan–Meier curves based on fifths of the predicted risk probability estimated from the baseline clinical model alone in all 8401 participants. B, Similar curves based on the fifths of the risk probability estimated from a model that that includes the baseline clinical variables plus the levels of the 10 identified biomarkers in Table 3.

Calibration plots of the proportion of outcomes within each tenth (identified using deciles) of predicted risk (Figure 2) suggested that the predicted risk somewhat underestimates the true risk of the CV composite, expanded CV composite, and death with slopes of 0.77, 0.85, and 0.88, respectively (Figure 2). However, these slopes and the significant (P<0.0001) optimism-adjusted integrated discrimination improvement (Table V in the online-only Data Supplement) reflect the improved performance achieved by addition of the biomarkers.

Calibration plots of the proportion of outcomes within each tenth (identified using deciles) of predicted risk based on the full models derived from all 8401 participants for the cardiovascular (CV) composite, expanded CV composite, and death.

Discussion

This analysis of 237 biomarkers identified a novel combination of 10 biomarkers that (when added to clinical risk factors) can discriminate dysglycemic people at higher versus lower risk of myocardial infarction, stroke, or CV death. The consequent increase in C statistic from 0.64 to 0.72 with nonoverlapping CIs is comparable to a 1.67-fold higher likelihood of the outcome.19 Moreover, these 10 plus an additional 5 biomarkers had the greatest impact on the ability to predict death with an increase in C statistic from 0.64 to 0.76. Conversely, 9 biomarkers only marginally improved the ability to predict an expanded CV composite outcome that included heart failure hospitalization or revascularization (Table 3).

Of all the identified biomarkers, the 3 (NT-proBNP, angiopoietin 2, and glutathione S transferase α) that were identified for all 3 of the outcomes in all of the analyses may have the most pathophysiologic and prognostic relevance for serious outcomes (Table III in the online-only Data Supplement). It is notable that 2 of these 3 biomarkers were positively associated with all 3 of the outcomes and have been implicated previously as either markers or possible mediators of CV disease. BNP is a cardiac hormone involved in blood pressure, volume, and sodium regulation. It is produced when its precursor hormone (proBNP), which is secreted by the ventricle in response to wall tension or ischemia (and oxidative stress), is cleaved into active BNP and an inactive amino-terminal fragment (NT-proBNP), which has a longer half-life and lesser fluctuation than BNP.20 Large epidemiologic analyses consistently reporting that elevated levels of NT-proBNP are strong independent risk factors for incident CV outcomes in people with and without established diabetes mellitus21–23 strongly support the current findings in people with impaired fasting glucose, impaired glucose tolerance, or early diabetes mellitus. The observation that its hazard ratio was greater than that of most of the other biomarkers further highlights its value for identifying high-risk individuals. Angiopoietin 2 is a cytokine produced by activated endothelial cells in response to hypoxia and inflammatory and other signals, including vascular endothelial growth factor.24,25 It is involved in the regulation of vascular permeability and angiogenesis, and elevated levels have been reported in people with dysglycemia, diabetic retinopathy, and CV disease.25–27 Moreover, circulating levels of angiopoietin 2 and its receptor, sTie-2, are heritable traits and are associated with CV disease risk factors.28

Glutathione S transferase α was found to be inversely associated with all 3 outcomes. It is a cytosolic enzyme that biotransforms and detoxifies a variety of electrophilic substrates by conjugating them with the reduced form of glutathione.29 Although no link between an elevated glutathione S transferase α level and reduced CV outcomes has been previously reported, higher levels may be associated with reduced oxidative stress30 and have been noted in response to insulin administration.31

Strengths of this analysis include the standardized collection, processing, and storage of serum from consenting participants; the large number of participants from numerous ethnic groups who both did and did not develop one of the serious health outcomes during >6 years of follow-up; international representation of participants; the numbers of biomarkers that were assayed in stored sample from these participants; the fact that an analytic plan for this analysis was finalized before the biomarkers were measured; the routine clinical and biochemical measurements that were available in all of the participants; and an analytic strategy that identified biomarkers that yielded more prognostic information than could be inferred from these routinely measured variables alone. Despite these strengths, analyses of this sort should be viewed as hypothesis generating and may identify spurious signals that may not be replicable. The observation that identification of some of the biomarkers is affected by the number of participants analyzed (and therefore the number of outcomes) and the routine clinical measurements that are forced into the model highlights this point. It also suggests that those biomarkers that are identified by only 1 approach may have a more tenuous link to the outcome than those identified using a variety of approaches. Finally, these findings were based on analyses of people with moderate degrees of dysglycemia at baseline and may not apply to normoglycemic individuals, younger individuals, or those at low risk for CV outcomes.

These results highlight the potential value of analyses of stored blood from prospective epidemiological studies and randomized clinical trials for identifying people at the highest risk of future outcomes. Future analyses of genetic material from more than 5000 of these participants are currently underway and (when linked to the current biomarker database) will identify genetic determinants of these biomarkers and their serum levels. They will also characterize the prognostic value of combining genetic and biochemical biomarkers, generate biological hypotheses regarding the pathophysiology of death and CV events in people with dysglycemia, and (using mendelian randomization approaches) help determine whether the biomarkers are causally related to outcomes.

Sources of Funding

The ORIGIN trial and biomarker project were supported by Sanofi. The biomarker project was led by ORIGIN investigators at the Population Health Research Institute with the active collaboration of Sanofi scientists. Sanofi directly compensated Myriad RBM Inc for measurement of the biomarker panel and the Population Health Research Institute for scientific, methodologic, and statistical work.

Disclosures

Dr Gerstein has received consulting fees from Sanofi, Novo Nordisk, Lilly, AstraZeneca, Boehringer Ingelheim, and GlaxoSmith-Kline and support for research or continuing education through his institution from Sanofi, Lilly, Takeda, Novo Nordisk, Boehringer Ingelheim, and AstraZeneca. Dr Paré has received consulting fees from Sanofi, Bristol Myers Squibb, Lexicomp, and Amgen and support for research through his institution from Sanofi. Dr McQueen has received research support from Sanofi, Roche Diagnostics, and Abbott Diagnostics. Drs Haenel and Hess are employees of Sanofi. Dr Maggioni has received consulting fees from AstraZeneca, Sanofi, and Lilly and support for research from Boehringer Ingelheim. Dr Pogue has received consulting fees from Sanofi, and Dr Yusuf has received research support for ORIGIN from Sanofi through his institution. Dr Lee reports no conflicts.

. Relation between age and cardiovascular disease in men and women with diabetes compared with non-diabetic people: a population-based retrospective cohort study.Lancet. 2006;368:29–36. doi: 10.1016/S0140-6736(06)68967-8.

; EpiDREAM Investigators. Glucose levels are associated with cardiovascular disease and death in an international cohort of normal glycaemic and dysglycaemic men and women: the EpiDREAM cohort study.Eur J Prev Cardiol. 2012;19:755–764. doi: 10.1177/1741826711409327.

Origin Trial Investigators. Rationale, design, and baseline characteristics for a large international trial of cardiovascular disease prevention in people with dysglycemia: the ORIGIN Trial (Outcome Reduction with an Initial Glargine Intervention).Am Heart J. 2008;155:26–32.

Clinical Perspective

People with diabetes mellitus and nondiabetic dysglycemia are at high risk for cardiovascular (CV) outcomes or death, and demographic, clinical, and biochemical indices (eg, older age, male sex, high blood pressure, and a high hemoglobin A1c) can differentiate people at lower versus higher risk of these outcomes. The observation that combinations of these indexes provides better differentiation than any one index suggests that there may be many unrecognized CV biomarkers that could be measured in serum and that could better identify people most likely to have a CV outcome. Stored serum from 8401 participants in the recently completed Outcome Reduction With Initial Glargine Intervention trial was therefore assayed for 237 biomarkers that other research had linked to CV disease. Those biomarkers that independently predicted incident CV outcomes during >6 years of follow-up and that provided prognostic information in addition to that provided by classic clinical risk factors were identified using Cox regression models. Ten novel biomarkers were identified that were each independent determinants of the 1405 CV composite outcomes of nonfatal myocardial infarction, nonfatal stroke, or CV death. Moreover, 9 biomarkers (including 8 of these 10) were identified as independent determinants of the 2435 expanded composite outcomes that included heart failure hospitalization and revascularization, and 15 (including the 10 CV composite biomarkers) were identified that were independent determinants of the 1340 deaths. Thus, classic risk factors plus novel biomarkers can be used together to refine the CV prognosis of people with dysglycemia.